Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding infrastructure. Control barrier functions (CBFs) provide an effective mechanism for safety filtering, and recent CBF-based reinforcement learning (RL) methods embed such...
Sanghyuk Park, Kwan-Woo Lee, Taekyung Kim et al.· 0 citations
Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints e...
Yi-Qi Zhao, Taekyung Kim, Hideki Okamoto et al.· 0 citations
Physical interactions can create future hazards that are not apparent from the robot's current geometric surroundings. We present a framework termed Predictive Semantic Safety (PSS), which connects visual physical reasoning to backup-based safety filtering. A vision-language model (VLM) predicts physical events and the...
Taekyung Kim, S. Fradi, Yan-Ning Dai et al.· 0 citations
Distributed Model-Based Diffusion is proposed, a distributed server-robot framework that decomposes the reverse diffusion process into local conditional reverse diffusion processes that enables each robot to iteratively perform denoising independently within its own control subspace while conditioning on the current tr...
Haejoon Lee, Xin-Yi Wang, Taekyung Kim et al.· arXiv.org· 0 citations
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